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Chapter 2

Chapter 2. Introduction to Probability. Chapter 2 Introduction to Probability. Experiments and the Sample Space. Assigning Probabilities to Experimental Outcomes. Events and Their Probabilities. Some Basic Relationships of Probability. Bayes’ Theorem. Simpson’s Paradox.

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Chapter 2

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  1. Chapter 2 Introduction to Probability

  2. Chapter 2 Introduction to Probability • Experiments and the Sample Space • Assigning Probabilities to Experimental Outcomes • Events and Their Probabilities • Some Basic Relationships of Probability • Bayes’ Theorem • Simpson’s Paradox

  3. What are the chances that sales will decrease if we increase prices? What are the odds that a new investment will be profitable? Uncertainties Managers often base their decisions on an analysis of uncertainties such as the following: What is the likelihood a new assembly method method will increase productivity?

  4. Probability Probability is a numerical measure of the likelihood that an event will occur. Probability values are always assigned on a scale from 0 to 1. A probability near zero indicates an event is quite unlikely to occur. A probability near one indicates an event is almost certain to occur.

  5. Probability as a Numerical Measureof the Likelihood of Occurrence Increasing Likelihood of Occurrence 0 .5 1 Probability: The event is very unlikely to occur. The occurrence of the event is just as likely as it is unlikely. The event is almost certain to occur.

  6. Statistical Experiments In statistics, the notion of an experiment differs somewhat from that of an experiment in the physical sciences. In statistical experiments, probability determines outcomes. Even though the experiment is repeated in exactly the same way, an entirely different outcome may occur. For this reason, statistical experiments are some- times called random experiments.

  7. An Experiment and Its Sample Space An experimentis any process that generates well- defined outcomes. The sample space for an experiment is the set of all experimental outcomes. An experimental outcome is also called a sample point.

  8. An Experiment and Its Sample Space Experiment Toss a coin Inspection a part Conduct a sales call Roll a die Play a football game Sample Space Head, tail Defective, non-defective Purchase, no purchase 1, 2, 3, 4, 5, 6 Win, lose, tie

  9. 0 <P(Ei) < 1 for all i Assigning Probabilities • Basic Requirements for Assigning Probabilities 1. The probability assigned to each experimental outcome must be between 0 and 1, inclusively. where: Ei is the ith experimental outcome and P(Ei) is its probability

  10. P(E1) + P(E2) + . . . + P(En) = 1 Assigning Probabilities • Basic Requirements for Assigning Probabilities 2. The sum of the probabilities for all experimental outcomes must equal 1. where: n is the number of experimental outcomes

  11. Assigning Probabilities Classical Method Assigning probabilities based on the assumption of equally likely outcomes Relative Frequency Method Assigning probabilities based on experimentation or historical data Subjective Method Assigning probabilities based on judgment

  12. Classical Method • Example: Rolling a Die If an experiment has n possible outcomes, the classical method would assign a probability of 1/n to each outcome. Experiment: Rolling a die Sample Space: S = {1, 2, 3, 4, 5, 6} Probabilities: Each sample point has a 1/6 chance of occurring

  13. Relative Frequency Method • Example: Lucas Tool Rental Lucas Tool Rental would like to assign probabilities to the number of car polishers it rents each day. Office records show the following frequencies of daily rentals for the last 40 days. Number of Polishers Rented Number of Days 0 1 2 3 4 4 6 18 10 2

  14. Relative Frequency Method • Example: Lucas Tool Rental Each probability assignment is given by dividing the frequency (number of days) by the total frequency (total number of days). Number of Polishers Rented Number of Days Probability 0 1 2 3 4 4 6 18 10 2 40 .10 .15 .45 .25 .05 1.00 4/40

  15. Subjective Method • When economic conditions and a company’s • circumstances change rapidly it might be • inappropriate to assign probabilities based solely on • historical data. • We can use any data available as well as our experience and intuition, but ultimately a probability value should express our degree of belief that the experimental outcome will occur. • The best probability estimates often are obtained by • combining the estimates from the classical or relative • frequency approach with the subjective estimate.

  16. Subjective Method • Example: House Offer Consider the case in which Tim and Judy Elsmore justmade an offer to purchase a house. Two outcomes arepossible: E1 = their offer is acceptedE2 = their offer is rejected Judy believes the probability their offer will be accepted is 0.8; thus, Judy would set P(E1) = 0.8 and P(E2) = 0.2. Tim, however, believes the probability that their offer will be accepted is 0.6; hence, Tim would set P(E1) = 0.6 and P(E2) = 0.4. Tim’s probability estimate for E1 reflects a greater pessimism that their offer will be accepted.

  17. Events and Their Probabilities An eventis a collection of sample points. The probability of any event is equal to the sum of the probabilities of the sample points in the event. If we can identify all the sample points of an experiment and assign a probability to each, we can compute the probability of an event.

  18. Events and Their Probabilities • Example: Rolling a Die Event E = Getting an even number when rolling a die E = {2, 4, 6} P(E) = P(2) + P(4) + P(6) = 1/6 + 1/6 + 1/6 = 3/6 = .5

  19. Some Basic Relationships of Probability There are some basic probability relationships that can be used to compute the probability of an event without knowledge of all the sample point probabilities. Complement of an Event Addition Law Conditional Probability Multiplication Law

  20. Some Basic Relationships of Probability • Example: Bradley Investments Bradley has invested in two stocks, Markley Oil and Collins Mining. Bradley has determined that the possible outcomes of these investments three months from now are as follows. Investment Gain or Loss in 3 Months (in $000) Collins Mining Markley Oil 8 -2 10 5 0 -20

  21. Some Basic Relationships of Probability • Example: Bradley Investments An analyst made the following probability estimates. Exper. Outcome Net Gain or Loss Probability (10, 8) (10, -2) (5, 8) (5, -2) (0, 8) (0, -2) (-20, 8) (-20, -2) $18,000 Gain $8,000 Gain $13,000 Gain $3,000 Gain $8,000 Gain $2,000 Loss $12,000 Loss $22,000 Loss .20 .08 .16 .26 .10 .12 .02 .06

  22. Tree Diagram • Example: Bradley Investments Collins Mining (Stage 2) Markley Oil (Stage 1) Experimental Outcomes Gain 8 (10, 8) Gain $18,000 (10, -2) Gain $8,000 Lose 2 Gain 10 (5, 8) Gain $13,000 Gain 8 (5, -2) Gain $3,000 Lose 2 Gain 5 Gain 8 (0, 8) Gain $8,000 Even (0, -2) Lose $2,000 Lose 2 Lose 20 Gain 8 (-20, 8) Lose $12,000 (-20, -2) Lose $22,000 Lose 2

  23. Events and Their Probabilities • Example: Bradley Investments Event M = Markley Oil Profitable M = {(10, 8), (10, -2), (5, 8), (5, -2)} P(M) = P(10, 8) + P(5, 8) + P(0, 8) + P(-20, 8) = .20 + .08 + .16 + .26 = .70

  24. Complement of an Event The complement of event A is defined to be the event consisting of all sample points that are not in A. The complement of A is denoted by Ac. Sample Space S Event A Ac Venn Diagram

  25. Union of Two Events The union of events A and B is the event containing all sample points that are in A or B or both. The union of events A and B is denoted by AB Sample Space S Event A Event B

  26. Union of Two Events • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable MC = Markley Oil Profitable or Collins Mining Profitable (or both) MC = {(10, 8), (10, -2), (5, 8), (5, -2), (0, 8), (-20, 8)} P(MC) =P(10, 8) + P(10, -2) + P(5, 8) + P(5, -2) + P(0, 8) + P(-20, 8) = .20 + .08 + .16 + .26 + .10 + .02 = .82

  27. Intersection of Two Events The intersection of events A and B is the set of all sample points that are in both A and B. The intersection of events A and B is denoted by A Sample Space S Event A Event B Intersection of A and B

  28. Intersection of Two Events • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable MC = Markley Oil Profitable and Collins Mining Profitable MC = {(10, 8), (5, 8)} P(MC) =P(10, 8) + P(5, 8) = .20 + .16 = .36

  29. Addition Law The addition law provides a way to compute the probability of event A, or B, or both A and B occurring. The law is written as: P(AB) = P(A) + P(B) -P(AB

  30. Addition Law • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable MC = Markley Oil Profitable or Collins Mining Profitable We know: P(M) = .70, P(C) = .48, P(MC) = .36 Thus: P(MC) = P(M) + P(C) -P(MC) = .70 + .48 - .36 = .82 (This result is the same as that obtained earlier using the definition of the probability of an event.)

  31. Mutually Exclusive Events Two events are said to be mutually exclusive if the events have no sample points in common. Two events are mutually exclusive if, when one event occurs, the other cannot occur. Sample Space S Event A Event B

  32. Mutually Exclusive Events If events A and B are mutually exclusive, P(AB = 0. The addition law for mutually exclusive events is: P(AB) = P(A) + P(B) There is no need to include “-P(AB”

  33. Conditional Probability The probability of an event given that another event has occurred is called a conditional probability. The conditional probability of A given B is denoted by P(A|B). A conditional probability is computed as follows :

  34. = Collins Mining Profitable given Markley Oil Profitable Conditional Probability • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable We know: P(MC) = .36, P(M) = .70 Thus:

  35. Multiplication Law The multiplication law provides a way to compute the probability of the intersection of two events. The law is written as: P(AB) = P(B)P(A|B)

  36. Multiplication Law • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable MC = Markley Oil Profitable and Collins Mining Profitable We know: P(M) = .70, P(C|M) = .5143 Thus: P(MC) = P(M)P(M|C) = (.70)(.5143) = .36 (This result is the same as that obtained earlier using the definition of the probability of an event.)

  37. Marginal Probabilities (appear in the margins of the table) Joint Probability Table Collins Mining Profitable (C) Not Profitable (Cc) Markley Oil Profitable (M) Not Profitable (Mc) Total .70 .30 1.00 .36 .34 .12 .18 Total .48 .52 Joint Probabilities (appear in the body of the table)

  38. Independent Events If the probability of event A is not changed by the existence of event B, we would say that events A and B are independent. Two events A and B are independent if: P(A|B) = P(A) P(B|A) = P(B) or

  39. Multiplication Lawfor Independent Events The multiplication law also can be used as a test to see if two events are independent. The law is written as: P(AB) = P(A)P(B)

  40. Multiplication Lawfor Independent Events • Example: Bradley Investments Event M = Markley Oil Profitable Event C = Collins Mining Profitable Are events M and C independent? DoesP(MC) = P(M)P(C) ? We know: P(MC) = .36, P(M) = .70, P(C) = .48 But: P(M)P(C) = (.70)(.48) = .34, not .36 Hence: M and C are not independent.

  41. Mutual Exclusiveness and Independence Do not confuse the notion of mutually exclusive events with that of independent events. Two events with nonzero probabilities cannot be both mutually exclusive and independent. If one mutually exclusive event is known to occur, the other cannot occur.; thus, the probability of the other event occurring is reduced to zero (and they are therefore dependent). Two events that are not mutually exclusive, might or might not be independent.

  42. Bayes’ Theorem • Often we begin probability analysis with initial or • prior probabilities. • Then, from a sample, special report, or a product test we obtain some additional information. • Given this information, we calculate revised or posterior probabilities. • Bayes’ theorem provides the means for revising the prior probabilities. Prior Probabilities New Information Application of Bayes’ Theorem Posterior Probabilities

  43. Bayes’ Theorem • Consider a manufacturing firm that receives shipments of parts from two different suppliers: • A1denote the event that a part is from supplier 1 • A2denote the event that a part is from supplier 2 Currently, • 65% of the parts purchased are from supplier 1 • Remaining 35% are from supplier 2. • Thus, if a part is selected at random, we would assign the prior probabilities P(A1) = 0.65 and P(A2) = 0.35. • Example: Quality of Purchased Parts

  44. Bayes’ Theorem • Example: Quality of Purchased Parts The quality of the purchased parts varies with the source of supply. Let • Gdenote the event that a part is good • Bdenote the event that a part is bad. Based on historical data, the conditional probabilities of receiving good and bad parts from the two suppliers are: P(G | Al) = 0.98 and P(B | A1) = 0.02 P(G | A2) = 0.95 and P(B | A2) = 0.05

  45. Tree Diagram • Example: Quality of Purchased Parts Part Quality Supplier Experimental Outcomes P(G|A1) = .98 P(A1G) = .6370 P(A1) = .65 P(A1B) = .0130 P(B|A1) = .02 P(G|A2) = .95 P(A2G) = .3325 P(A2) = .35 P(A2B) = .0175 P(B|A2) = .05

  46. New Information A bad part caused one machine to break down. What is the probability that the bad part came from supplier 1 What is the probability that it came from supplier 2? With the information in the probability tree, we can use Bayes’ theorem to answer these questions. • Example: Quality of Purchased Parts

  47. Bayes’ Theorem • To find the posterior probability that event Ai will • occur given that event B has occurred, we apply • Bayes’ theorem. • Bayes’ theorem is applicable when the events for • which we want to compute posterior probabilities • are mutually exclusive and their union is the entire • sample space.

  48. Posterior Probabilities Given that the part received was bad, we revise the prior probabilities as follows: • Example: Quality of Purchased Parts = .4262

  49. Bayes’ Theorem: Tabular Approach • Example: Quality of Purchased Parts • Step 1 Prepare the following three columns: Column 1- The mutually exclusive events for which posterior probabilities are desired. Column 2- The prior probabilities for the events. Column 3- The conditional probabilities of the new information given each event.

  50. Bayes’ Theorem: Tabular Approach • Example: Quality of Purchased Parts • Step 1 (1) (2) (3) (4) (5) Prior Probabilities P(Ai) Conditional Probabilities P(B|Ai) Events Ai .02 .05 A1 A2 .65 .35 1.00

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